The visual / inertial positioning method represented by the visual / inertial odometry has been widely used recently. The traditional visual / inertial odometry obtains the fixed distortion parameters of camera through offline calibration methods. When the calibration of camera distortion parameters is inaccurate or the camera distortion parameters are changed, the positioning accuracy will decrease. Aiming at this problem, a robust visual / inertial odometry method facing to online self-calibration of camera distortion parameters is proposed. Firstly, the camera distortion parameters are added to the variables to be optimized in the visual / inertial odometry. The Jacobian matrix of the visual reprojection error with respect to the variables to be optimized is derived. Then through the factor graph optimization method, online self-calibration of camera distortion parameters and real-time optimization solution of carrier navigation information are realized. Finally, the effectiveness of the proposed method was verified through the EuRoC dataset and realworld experiments. The real-word experiment result demonstrates that compared with the traditional visual / inertial odometry method, the accuracy of the proposed method is improved by 65. 40% in an outdoor wide open scene.